MLP Can Be A Good Transformer Learner
Paper
• 2404.05657 • Published
• 1
Papers - Custom Layers - MLP
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Toward a Better Understanding of Fourier Neural Operators: Analysis and Improvement from a Spectral Perspective
Paper • 2404.07200 • Published • 2 -
An inclusive review on deep learning techniques and their scope in handwriting recognition
Paper • 2404.08011 • Published • 1 -
Long-form music generation with latent diffusion
Paper • 2404.10301 • Published • 27 -
MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs
Paper • 2402.15627 • Published • 36 -
Scaling MLPs: A Tale of Inductive Bias
Paper • 2306.13575 • Published • 17 -
MeshLRM: Large Reconstruction Model for High-Quality Mesh
Paper • 2404.12385 • Published • 27 -
GLIGEN: Open-Set Grounded Text-to-Image Generation
Paper • 2301.07093 • Published • 4 -
Transformers Can Represent n-gram Language Models
Paper • 2404.14994 • Published • 21 -
Caduceus: Bi-Directional Equivariant Long-Range DNA Sequence Modeling
Paper • 2403.03234 • Published • 14 -
KAN: Kolmogorov-Arnold Networks
Paper • 2404.19756 • Published • 116 -
Unraveling the Gradient Descent Dynamics of Transformers
Paper • 2411.07538 • Published • 2 -
An Evolved Universal Transformer Memory
Paper • 2410.13166 • Published • 6